{"id":"W4320477126","doi":"10.1016/j.engappai.2023.105834","title":"Data-driven failure prediction of Fiber-Reinforced Polymer composite materials","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Composite laminates; Computer science; Fibre-reinforced plastic; Artificial neural network; Composite number; Test data; Fiber; Materials science; Composite material; Structural engineering; Artificial intelligence; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001540073,0.0001375246,0.0002142415,0.0002479792,0.00003786223,0.00001394222,0.0005023594,0.00009283683,0.00004212422],"category_scores_gemma":[0.00002713247,0.0001561711,0.00002989134,0.0006830348,0.00004349117,0.0001346652,0.0001015346,0.0001079528,0.00006331269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003364701,"about_ca_system_score_gemma":0.000012626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004553772,"about_ca_topic_score_gemma":0.000001271308,"domain_scores_codex":[0.9988432,0.000007345012,0.0005971227,0.0001810153,0.0001690612,0.0002022347],"domain_scores_gemma":[0.9989979,0.0001052901,0.00007901113,0.0006924295,0.00007139482,0.00005400341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005693586,0.000006105417,0.00001187882,0.0003754417,0.00003409874,3.305202e-7,0.0001425513,0.3177871,0.6501174,0.007125596,0.0003559456,0.02403787],"study_design_scores_gemma":[0.00000834117,0.0000150821,0.000390552,0.00005972215,0.00001137449,0.000001465084,0.00002737892,0.1991971,0.7993278,0.0001203016,0.000747072,0.00009385988],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6210895,0.0001294347,0.3726942,0.00007869908,0.0005994204,0.0008922362,0.001497772,0.002936413,0.00008234019],"genre_scores_gemma":[0.9839025,0.0000497069,0.0153986,0.000001134272,0.000166444,0.0001326554,0.0002778799,0.00003639326,0.00003465352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3628131,"threshold_uncertainty_score":0.6368471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882404992180223,"score_gpt":0.3003944498353799,"score_spread":0.2615703999135777,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}